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Automatic Detection of Dental Artifact in a Fully-Automated Treatment Planning Workflow

S Hernandez*, C Sjogreen, S Gay, T Netherton, A Olanrewaju, C Nguyen, D Rhee, J Mendez, L Court, C Cardenas, MD Anderson Cancer Center, Houston, TX

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: The Radiation Planning Assistant (RPA), is a web-based tool that will provide automated treatment planning for resource-constrained clinics. As part of this workflow, the user must re-calculate the final dose in their own TPS. We investigated the optimal workflow for auto-planning in the presence of dental artifacts.


Methods: We (1)developed an automatic dental artifact identification tool and (2)assessed its use in an automated workflow. (1)Over 80,000 HN CT slices (549 patients) were manually annotated by three users and majority-voting was applied to define the presence or absence of dental artifact. The patients were sub-divided into train, cross-validation, and test datasets (3:1:1 respectively). Since <4% of slices had artifact, a random subset of CT slices without dental artifact was used to balance the classes (1:1) in the training dataset. The Inception-V3 deep learning model was trained with a binary cross-entropy loss function. (2) Using this model, we investigated various density override methods applied pre- and post-optimization on 15 independent HN CT scans. The effects of methods on D-max, dose to normal structures, and V95 for PTV1 were quantified.


Results: Sensitivity/specificity on a per-slice-basis were 91%/99%. The model identified all patients with artifact and never misassigned artifact. Small dosimetric differences were observed between the various density-override methods (±1%). Applying such methods pre-optimization resulted in an average reduction of 1.5% to D-max. For the pre- and post-optimized plans, 57% and 94%, respectively, of dose comparisons resulted in normal structure dose differences of ±1%. Majority of differences in V95[%] were within ±0.2% for both methods.


Conclusion: Dose differences across multiple density-override methods were small. However, applying dental artifact management before plan optimization resulted in a more appreciable dosimetric impact relative to after. Therefore, we plan to offer RPA users these options to implement their choice of dental artifact management prior to plan optimization.

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Funding Support, Disclosures, and Conflict of Interest: Our research group receives funding from the NCI and Varian Medical Systems

Keywords

Treatment Planning, Image Artifacts, Image Analysis

Taxonomy

Not Applicable / None Entered.

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